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chapter 11, Block Truncation Coding
Table of Contents
- I Background
- 1. Digital Images and Image Compression
- II Information Theory Concepts
- 2. Source Models and Entropy
- III Lossless Compression Techniques
- 6. Bit Plane Encoding
- IV Lossy Compression Techniques
- 9. Lossy Predictive Coding
- 10. Transform Coding
- 13. Subband Coding
Chapter Contents
- 11.1 Quantizer Design
- 11.1.1 Moment-preserving quantizers
- 11.1.2 Error-minimizing quantizers
- 11.2 Source Coding of Bit Map and Reconstruction Levels
- 11.2.1 Reduced bit representation/joint quantization
- 11.2.2 Vector quantization encoding of reconstruction levels
- 11.2.3 Bit map omission
- 11.2.4 Independent/dependent bits
- 11.2.5 VQ encoding of bit map
- 11.3 Adaptive Block Size BTC
- 11.4 BTC Results
- 11.5 Implementation Issues/Complexity of Adaptive AMBTC
Excerpt
In block truncation coding (BTC), an image is segmented into n × n (typically, 4 × 4) nonoverlapping blocks of pixels, and a two-level (one-bit) quantizer is independently designed for each block. Both the quantizer threshold and the two reconstruction levels are varied in response to the local statistics of a block. Thus, encoding is essentially a local binarization process, and the representation of a block consists of an n × n bit map indicating the reconstruction level associated with each pixel and overhead information specifying the two reconstruction levels. Decoding is the simple process of placing the appropriate reconstruction value at each pixel location as per the bit map. A diagram of the basic BTC scheme is shown in Fig. 11.1.
©1991 Society of Photo-Optical Instrumentation Engineers
